Exploring the bibliometric impact of artificial intelligence in radiology: An analytical approach

Abstract

• AI is revolutionizing healthcare, particularly radiology, by influencing research and clinical practice. • The study presents a bibliometric analysis of AI’s impact on radiology from 2018 to 2022. • AI-related papers in radiology have higher citation indices, indicating their significant impact. • There’s an upward trend in AI-related publications in radiology over the study period. • The study underscores AI’s pivotal role in shaping research trajectories in radiology. Artificial intelligence (AI) is revolutionizing operations worldwide and is particularly transforming radiology. AI has a key role in enhancing diagnostic accuracy, workflow efficiency, and research output in radiology. This article presents a comprehensive bibliometric analysis of the influence of AI on radiology over five years (2018–2022). Reports published between 2018 and 2022 were identified through the Scopus database and categorized based on the AI methodologies employed. The study presents the volume and distribution of studies on AI, identifies publication patterns by country, and measures the impact of studies in terms of citation counts and field-weighted citation indices (FWCIs). Field-weighted view impact (FWVI), a field-normalized view metric that estimates the visibility of studies and the accessibility and engagement of AI studies in radiology, is used in this study. Compared with non-AI studies, the United States leads radiology-related AI publications, with AI-based articles having higher citation indices. The findings reveal a strong increasing trend for AI-related studies over the duration of the study. Moreover, open-access AI publications are found to have higher FWVI scores than subscription-based articles with greater visibility and higher readership. This paper highlights the growing dominance of AI in radiology and how it is influencing trends in clinical development and research. Through publication increase, citation impact, and study availability, this paper provides informative insight into how AI radiology studies are developing.

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